Uncertainty-Aware Transfer for Real-World Robotics

This review examines uncertainty-aware transfer for real-world robotics. The organizing question is which uncertainty signals should trigger adaptation, additional sensing, human input, or abstention. Ten related scholarly sources are synthesized through a decision-centered framework spanning problem definition, mechanism, measurement, evaluation, implementation, and governance. The review does not invent experiments, pooled estimates, or unreported quantitative results. It instead evaluates the strength and transferability of the available evidence, with particular attention to interpreting successful transfer on a narrow benchmark as broad operational readiness. The resulting framework links technical or empirical performance to explicit use conditions and identifies tests that should precede wider adoption in robot learning across simulation and physical environments.

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